{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coordinates and Dimensions of ECCOv4 NetCDF files\n",
    "\n",
    "## Objectives\n",
    "\n",
    "Introduce the student ot the idea that the ECCO v4 NetCDF fields have coordinate labels which shows *where* these fields are on the Arakawa-C grid.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The ECCOv4 files are provided as NetCDF files.  The file you have may look a little different than the ones shown here because we have been working hard at improving how what exactly goes into our NetCDF files.  \n",
    "\n",
    "This tutorial document is current as of Sep 2019 with the ECCOv4 NetCDF grid files provided in the following directories:\n",
    "\n",
    "https://ecco.jpl.nasa.gov/drive/files/Version4/Release3_alt (po.daac drive, recommended)\n",
    "\n",
    "https://web.corral.tacc.utexas.edu/OceanProjects/ECCO/ECCOv4/Release3_alt/  (mirror at U. Texas, Austin)\n",
    "\n",
    "\n",
    "As we showed in the first tutorial, we can use the `open_dataset` method from `xarray` to load a NetCDF tile file into Python as a `Dataset` object.  `open_dataset` is very convenient because it automatically parses the NetCDF file and constructs a `Dataset` object using all of the  dimensions, coordinates, variables, and metadata information.  \n",
    "\n",
    "In the last tutorial we analyzed the contents of a single ECCOv4 file, the 2010 monthly-averaged potential temperature.  Let's load it up again and take a closer look at its coordinates.  This time we'll name the new `Dataset` object *theta_dataset* since we are loading the file using `open_dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import the ecco_v4_py library into Python\n",
    "## =========================================\n",
    "## -- If ecco_v4_py is not installed in your local Python library, \n",
    "##    tell Python where to find it.  For example, if your ecco_v4_py\n",
    "##    files are in /Users/ifenty/ECCOv4-py/ecco_v4_py, then use:\n",
    "sys.path.append('/home/ifenty/ECCOv4-py')\n",
    "\n",
    "import ecco_v4_py as ecco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set top-level file directory for the ECCO NetCDF files\n",
    "## =================================================================\n",
    "# base_dir = '/home/username/'\n",
    "base_dir = '/home/ifenty/ECCOv4-release'\n",
    "\n",
    "## define a high-level directory for ECCO fields\n",
    "ECCO_dir = base_dir + '/Release3_alt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOAD NETCDF FILE\n",
    "## ================\n",
    "\n",
    "# directory containing the file\n",
    "data_dir= ECCO_dir + '/nctiles_monthly/THETA/'\n",
    "\n",
    "# filename\n",
    "fname = 'THETA_2010.nc'\n",
    "\n",
    "# load the file\n",
    "theta_dataset = xr.open_dataset(data_dir + fname).load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get started, plot the temperature field at the surface layer (k=0) for tile 2, NE Atlantic..\n",
    "\n",
    "> Note :: Don't worry about the complicated looking code below, w'll cover plotting later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f85134ef160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "theta_dataset.THETA.isel(k=0,tile=2,time=0).plot(vmin=-2, vmax=25, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dimensions and Coordinates of *THETA*\n",
    "\n",
    "Let's take a closer look at what is inside this dataset.  We suppress the metadata (attrs) just to reduce how much is printed to the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, k: 50, nv: 2, tile: 13, time: 12)\n",
       "Coordinates:\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    hFacC      (tile, k, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    THETA      (time, tile, k, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.attrs = []\n",
    "theta_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensions\n",
    "\n",
    "*theta_dataset* shows six dimensions, **i**, **j**, **k**, **tile**, **time**, and **nv**.  Recall that ``Dataset`` objects are containers and so it lists all of the  **unique** dimensions of the variables it is storing.  *theta_dataset* is storing a single *Data variable*, *THETA*.  We see that this *THETA* field is five dimensional from the (**tile**, **time**, **k**, **j**, **i**) in the line:\n",
    "~~~\n",
    "THETA      (time, tile, k, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
    "~~~\n",
    "Examining the coordinates of the ``Dataset``, we find that all of them have some combination of the six dimensions.  Note **nv** is not a spatial or temporal dimension per se. It is a kind of dummy dimension of length 2 for the coordiante **time_bnds** which has both a starting and ending time for each one averaging period.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coordinates\n",
    "\n",
    "#### Dimension Coordinates\n",
    "\n",
    "Beyond having three spatial dimensions *theta_dataset* also has *coordinates* in the **i**, **j**, and **k** directions.  The most basic coordinates are 1D vectors, one for each dimension, which contain *indices* for the array.  Let us call these basic coordinates, *dimension coordinates*.  Here we use 0 as the first index of dimension coordinates\n",
    "```\n",
    "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
    "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
    "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
    "```\n",
    "\n",
    "Let's examine each *Dimension Coordinate* more closely\n",
    "\n",
    "##### Dimension Coordinate ``i``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'i' (i: 90)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)\n",
       "Coordinates:\n",
       "  * i        (i) int32 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "Attributes:\n",
       "    long_name:      x-dimension of the t grid\n",
       "    standard_name:  x_grid_index\n",
       "    swap_dim:       XC\n",
       "    axis:           X"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*i* is an array of integers from 0 to 89 indicating the *x_grid_index* along this tile's *X* axis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dimension Coordinate ``j``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'j' (j: 90)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)\n",
       "Coordinates:\n",
       "  * j        (j) int32 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "Attributes:\n",
       "    long_name:      y-dimension of the t grid\n",
       "    standard_name:  y_grid_index\n",
       "    swap_dim:       YC\n",
       "    axis:           Y"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*j* is an array of integers from 0 to 89 indicating the *y_grid_index* along this tile's *Y* axis.\n",
    "\n",
    "##### Dimension Coordinate ``k``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'k' (k: 50)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)\n",
       "Coordinates:\n",
       "  * k        (k) int32 0 1 2 3 4 5 6 7 8 9 10 ... 40 41 42 43 44 45 46 47 48 49\n",
       "    Z        (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    PHrefC   (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    drF      (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "Attributes:\n",
       "    long_name:      z-dimension of the t grid\n",
       "    standard_name:  z_grid_index\n",
       "    swap_dim:       Z\n",
       "    axis:           Z"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*k* is an array of integers from 0 to 49 indicating the *z_grid_index* along this tile's *Z* axis.\n",
    "\n",
    "##### Dimension Coordinate ``tile``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'tile' (tile: 13)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)\n",
       "Coordinates:\n",
       "  * tile     (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Attributes:\n",
       "    standard_name:  tile_index"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.tile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*tile* is an array of integers from 0 to 12, one for each tile of the lat-lon-cap grid.\n",
    "\n",
    "##### Dimension Coordinate ``time``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time' (time: 12)>\n",
       "array(['2010-01-16T12:00:00.000000000', '2010-02-15T12:00:00.000000000',\n",
       "       '2010-03-16T12:00:00.000000000', '2010-04-16T12:00:00.000000000',\n",
       "       '2010-05-16T12:00:00.000000000', '2010-06-15T12:00:00.000000000',\n",
       "       '2010-07-16T12:00:00.000000000', '2010-08-16T12:00:00.000000000',\n",
       "       '2010-09-15T12:00:00.000000000', '2010-10-16T12:00:00.000000000',\n",
       "       '2010-11-16T12:00:00.000000000', '2010-12-16T12:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    iter     (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time     (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Attributes:\n",
       "    long_name:      center time of averaging period\n",
       "    standard_name:  time\n",
       "    bounds:         time_bnds\n",
       "    axis:           T"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this file the *time* coordinate indicates the *center time of the averaging period*.  Recall that we loaded the monthly-mean ``THETA`` fields for  2010, so the *center time of the averaging periods* are the middle of each month in 2010.\n",
    "\n",
    "#### Other Coordinates\n",
    "\n",
    "Notice some *coordinates* do not have an **\"\\*\"** in front of their names:\n",
    "```             \n",
    "Coordinates:\n",
    "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
    "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
    "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
    "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
    "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
    "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
    "    hFacC      (tile, k, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
    "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
    "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
    "```\n",
    "\n",
    "These are so-called [non-dimension coordinates](http://xarray.pydata.org/en/stable/data-structures.html#coordinates).  From the xarray documenation:\n",
    "\n",
    "```\n",
    "1. non-dimension coordinates are variables that [may] contain coordinate data, \n",
    "   but are not a dimension coordinate. \n",
    "\n",
    "2. They can be multidimensional ... and there is no relationship between \n",
    "   the name of a non-dimension coordinate and the name(s) of its dimension(s). \n",
    "\n",
    "3. Non-dimension coordinates can be useful for indexing or plotting; ...\n",
    "```\n",
    "\n",
    "Four of these variables contain spatial coordinate data:\n",
    "\n",
    "* **XC** and **YC**, the longitude and latitudes of the 'c' points (varies with **tile**, **j** and **i**)\n",
    "* **Z** the center depth of tracer cells (varies only with **k**)\n",
    "* **PHrefC**, a reference pressure of tracer cells (varies only with **k**)\n",
    "\n",
    "Two of these variables have *spatial dimensions* but are not coordinates in a traditional sense (and they will be removed in future ECCOv4 releases because their presence here makes no sense).\n",
    "\n",
    "* **hFacC**, the fraction (0,1) of a tracer grid cell height which is wet (varies with **tile**, **k**, **j**, and **i**) \n",
    "* **drF**, the distance between the top and bottom of a tracer grid cell (varies only with **k**)\n",
    "* **rA**, model grid cell area (varies with **tile**, **j** and **i**)\n",
    "\n",
    "Three non-dimension coordinates do not have any spatial dimensions!  \n",
    "\n",
    "* **iter**, the time step of the model (time iteration) when the record was saved \n",
    "* **time**, a calendar date and time \n",
    "* **time_bnds**, a 1x2 array of calendar dates and times indicating the start and end times of the averaging period of the field\n",
    "\n",
    "When multiple DataArrays from different tiles and times are combined, the dimensions of the merged arrays will expand along the **time** and **tile** dimensions.   \n",
    "\n",
    "Let's quickly look at the **time_bnds** coordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time_bnds' (time: 12, nv: 2)>\n",
       "array([['2010-01-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000'],\n",
       "       ['2010-02-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000'],\n",
       "       ['2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000'],\n",
       "       ['2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000'],\n",
       "       ['2010-05-01T00:00:00.000000000', '2010-06-01T00:00:00.000000000'],\n",
       "       ['2010-06-01T00:00:00.000000000', '2010-07-01T00:00:00.000000000'],\n",
       "       ['2010-07-01T00:00:00.000000000', '2010-08-01T00:00:00.000000000'],\n",
       "       ['2010-08-01T00:00:00.000000000', '2010-09-01T00:00:00.000000000'],\n",
       "       ['2010-09-01T00:00:00.000000000', '2010-10-01T00:00:00.000000000'],\n",
       "       ['2010-10-01T00:00:00.000000000', '2010-11-01T00:00:00.000000000'],\n",
       "       ['2010-11-01T00:00:00.000000000', '2010-12-01T00:00:00.000000000'],\n",
       "       ['2010-12-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000']],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Attributes:\n",
       "    long_name:      time bounds of averaging period\n",
       "    standard_name:  time_bounds"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.time_bnds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For time-averaged fields, **time_bnds** is a 2D array provding the *start* and *end* time of each averaging period.  \n",
    "\n",
    "Let's look at the third record (March, 2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time_bnds' (nv: 2)>\n",
       "array(['2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    time_bnds  (nv) datetime64[ns] 2010-03-01 2010-04-01\n",
       "    iter       int32 159948\n",
       "    time       datetime64[ns] 2010-03-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Attributes:\n",
       "    long_name:      time bounds of averaging period\n",
       "    standard_name:  time_bounds"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dataset.time_bnds[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the time bounds are 2010-03-10 to 2010-04-01, which make sense.\n",
    "  \n",
    "Having this time-bounds information readily available can be very helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dimension Coordinates of the Arakawa C-Grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimension coordinates have special meanings.  The MITgcm uses the staggered Arakawa-C grid (hereafter c-grid). In c-grid models, variables are staggered in space.  Horizontally, variables are associated with three 'locations': \n",
    "\n",
    "1. tracer cells (e.g. temperature, salinity, density)\n",
    "2. the 4 lateral faces of tracer cells (e.g., horizontal velocities and fluxes) \n",
    "3. the 4 corners of tracer cells (e.g., vertical component of vorticity field)\n",
    "\n",
    "Vertically, there are also two 'locations': \n",
    "\n",
    "1. tracer cells\n",
    "2. the 2 top/bottom faces of tracer cells (e.g., vertical velocities and fluxes).\n",
    "\n",
    "To understand this better, let's review the geometry of c-grid models.\n",
    "\n",
    "### 3D staggering of velocity components. \n",
    "\n",
    "Paraphrasing from https://mitgcm.readthedocs.io/en/latest/algorithm/c-grid.html,\n",
    "\n",
    "~~~\n",
    "In c-grid models, the components of flow (u,v,w) are staggered in \n",
    "space such that the zonal component falls on the interface between \n",
    "tracer cells in the zonal direction. Similarly for the meridional \n",
    "and vertical directions. \n",
    "~~~\n",
    "\n",
    "#### Why the c-grid?\n",
    "\n",
    "~~~\n",
    "The basic algorithm employed for stepping forward the momentum \n",
    "equations is based on retaining non-divergence of the flow at \n",
    "all times. This is most naturally done if the components of flow\n",
    "are staggered in space in the form of an Arakawa C grid...\n",
    "~~~\n",
    "\n",
    "![uvw-grid](../figures/uvw.png )\n",
    "\n",
    "\n",
    "### Defining the c-grid coordinate system\n",
    "\n",
    "As shown, variables on Arakawa-C grids (c-grid) are staggered in space.  For convenience we  define a coordinate system that distinguishes between these different locations.\n",
    "\n",
    "#### The c-grid horizontal coordinates\n",
    "\n",
    "In the horizontal, variables can take one of four locations: **c**, **u**, **v**, or **g**.  \n",
    "\n",
    "![c-grid](../figures/C-grid-points.png )\n",
    "\n",
    "\n",
    "##### horizontal \"c\" location\n",
    "\n",
    "Variables associated with tracer cell area or volumetric averages (e.g., temperature, salinity, sea ice thickness, air-sea net heat flux) and variables associated with vertical velocities (e.g., $\\vec w$) are identified with **c** locations.\n",
    "\n",
    "For these variables we define the horizontal dimensions of **i** and **j**, corresponding with the model grid $x$ and $y$ directions, respectively.  \n",
    "\n",
    "##### horizontal \"u\" location\n",
    "\n",
    "Variables associated with the two lateral sides of tracer cells in the $x$ direction are identified with **u** locations. \n",
    "\n",
    "Define the horizontal dimensions of **u** variables as **i_g** and **j**, corresponding with the model's $x$ and $y$ dimensions, respectively.  \n",
    "\n",
    "> **Important Note**: In the llc90 curvilinear model grid used by ECCOv4, the $x$ direction is **NOT** same as the geographic zonal (east-west) direction.\n",
    "\n",
    "##### horizontal \"v\" location\n",
    "\n",
    "Variables associated with the two lateral sides of tracer cells in the $y$ direction are identified with **v** locations. \n",
    "\n",
    "Define the horizontal dimensions of **v** variables as **i** and **j_g**, corresponding with the model's $x$ and $y$ dimensions, respectively.\n",
    "\n",
    "> **Important Note**: In the llc90 curvilinear model grid used by ECCOv4, the $y$ direction is **NOT** same as the geographic meridional (north-south) direction.\n",
    "\n",
    "\n",
    "##### horizontal \"g\" location\n",
    "\n",
    "Variables associated with the horizontal corners of tracer grid cells are identified with **g** locations.\n",
    "\n",
    "Define the horizontal dimensions of **g** variables as **i_g** and **j_g**, corresponding with the model's $x$ and $y$ dimensions, respectively.  \n",
    "\n",
    "#### The c-grid vertical coordinates\n",
    "\n",
    "In the horizontal, variables can take one of two locations: **c** and **w**\n",
    "\n",
    "#### vertical \"c\" location\n",
    "\n",
    "Variables associated with tracer cell volumetric quantities (e.g., temperature, salinity) are identified with **c** locations.\n",
    "\n",
    "For these variables we define the vertical dimensions of **k** which corresponds with the model grid' $z$ direction.\n",
    "\n",
    "#### vertical \"w\" location\n",
    "\n",
    "Variables associated with the two top/bottom sides of tracer cells in the $z$ direction are identified with **w** locations. \n",
    "\n",
    "For these variables we define the vertical dimension of **$k_u$** to indicate the model tracer cell's **u**pper faces in the $k$ direction, respectively.  \n",
    "\n",
    "Two other vertical dimensions are also used.  **$k_l$** indicates the model tracer cell's **l**ower faces, and $k_{p1}$ which index all of the **upper** and **lower** faces.  \n",
    "\n",
    "> Note: In ECCOv4 NetCDF files both **$k_u$**(0) and **$k_{p1}$**(0) correspond to the same top face of the model tracer grid cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All ECCOv4 coordinates\n",
    "\n",
    "Now that we have been oriented to the dimensions and coordinates used by ECCOv4, let's load up and examinea a ``Dataset`` that uses all of them, an ECCOv4 NetCDF grid file. \n",
    "\n",
    "Open the ECCOv4 grid file associated with tile 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ifenty/ECCOv4-release/Release3_alt/nctiles_grid/\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_l: 50, k_p1: 51, k_u: 50, tile: 13)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50\n",
       "  * tile       (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    XC         (tile, j, i) float32 ...\n",
       "    YC         (tile, j, i) float32 ...\n",
       "    XG         (tile, j_g, i_g) float32 ...\n",
       "    YG         (tile, j_g, i_g) float32 ...\n",
       "    CS         (tile, j, i) float32 ...\n",
       "    SN         (tile, j, i) float32 ...\n",
       "    Z          (k) float32 ...\n",
       "    Zp1        (k_p1) float32 ...\n",
       "    Zu         (k_u) float32 ...\n",
       "    Zl         (k_l) float32 ...\n",
       "    rA         (tile, j, i) float32 ...\n",
       "    dxG        (tile, j_g, i) float32 ...\n",
       "    dyG        (tile, j, i_g) float32 ...\n",
       "    Depth      (tile, j, i) float32 ...\n",
       "    rAz        (tile, j_g, i_g) float32 ...\n",
       "    dxC        (tile, j, i_g) float32 ...\n",
       "    dyC        (tile, j_g, i) float32 ...\n",
       "    rAw        (tile, j, i_g) float32 ...\n",
       "    rAs        (tile, j_g, i) float32 ...\n",
       "    drC        (k_p1) float32 ...\n",
       "    drF        (k) float32 ...\n",
       "    PHrefC     (k) float32 ...\n",
       "    PHrefF     (k_p1) float32 ...\n",
       "    hFacC      (k, tile, j, i) float32 ...\n",
       "    hFacW      (k, tile, j, i_g) float32 ...\n",
       "    hFacS      (k, tile, j_g, i) float32 ...\n",
       "    maskC      (k, tile, j, i) bool ...\n",
       "    maskW      (k, tile, j, i_g) bool ...\n",
       "    maskS      (k, tile, j_g, i) bool ...\n",
       "    maskCtrlW  (k, tile, j, i_g) bool ...\n",
       "    maskCtrlS  (k, tile, j_g, i) bool ...\n",
       "    maskCtrlC  (k, tile, j, i) bool ...\n",
       "Data variables:\n",
       "    *empty*\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        netCDF wrapper of MITgcm MDS binary data\n",
       "    source:       MITgcm\n",
       "    history:      Created by calling `open_mdsdataset(extra_metadata=None, ll..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dir = ECCO_dir + '/nctiles_grid/'\n",
    "print(grid_dir)\n",
    "## load the grid\n",
    "grid_dataset = xr.open_dataset(grid_dir + 'ECCOv4r3_grid.nc')\n",
    "\n",
    "# show contents of grid_dataset\n",
    "grid_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensions\n",
    "`Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_l: 50, k_p1: 51, k_u: 50, tile: 13)`\n",
    "\n",
    "The *Dimensions* list now lists the six different coordinates and their dimension used by variables stored in this new grid tile `Dataset` object.  \n",
    "\n",
    "### Dimension Coordinates \n",
    "\n",
    "\n",
    "```\n",
    "  * k_p1     (k_p1) int64 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49 50\n",
    "  * j_g      (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * i_g      (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k        (k) int64 0 1 2 3 4 5 6 7 8 9 10 ... 40 41 42 43 44 45 46 47 48 49\n",
    "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k_u      (k_u) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
    "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k_l      (k_l) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
    "  * tile     (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
    "``` \n",
    "\n",
    "We note that horizontal coordindates have 90 indices [0..89], *ku* and *kl* have 50 [0..49] and *kp1* has 51 [0..50]\n",
    "\n",
    "\n",
    "### Non-Dimension Spatial Coordinates\n",
    "\n",
    "Some non-dimension coordinates are spatial coordinates.\n",
    "* XC, YC : longitude and latitude of tracer cell centers\n",
    "* XG, YG : longitude and latitude of tracer cell corners\n",
    "* Z_l, Z_u, Z_p1 : depths of tracer cell lower and upper faces\n",
    "* Z : depths of tracer cell centers\n",
    "* PHrefC, PHrefF : reference pressures at tracer cell centers and upper and lower faces\n",
    "\n",
    "```\n",
    "    XC       (tile, j, i) float32 -111.60647 -111.303 ... -105.58465 -111.86579\n",
    "    YC       (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
    "    XG       (tile, j_g, i_g) float32 -115.0 -115.0 ... -102.928925 -108.95171\n",
    "    YG       (tile, j_g, i_g) float32 -88.17569 -88.31587 ... -87.9892 -88.02409\n",
    "    Zl       (k_l) float32 0.0 -10.0 -20.0 -30.0 ... -4834.0 -5244.5 -5678.0\n",
    "    Zu       (k_u) float32 -10.0 -20.0 -30.0 -40.0 ... -5244.5 -5678.0 -6134.5\n",
    "    Z        (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
    "    Zp1      (k_p1) float32 0.0 -10.0 -20.0 -30.0 ... -5244.5 -5678.0 -6134.5\n",
    "    PHrefC   (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
    "    PHrefF   (k_p1) float32 0.0 98.1 196.2 ... 51448.547 55701.18 60179.445\n",
    "```\n",
    "\n",
    "While others describe the model grid geometry (areas, distances, distances, and geometric factors) and other information (model depth).  They are not coordinates in any meaningful sense but they *are essential for quantitative analysis*. \n",
    "\n",
    "horizontal distances:\n",
    "```\n",
    "    dxG      (j_g, i) float32 ...\n",
    "    dyG      (j, i_g) float32 ...\n",
    "    dxC      (j, i_g) float32 ...\n",
    "    dyC      (j_g, i) float32 ...\n",
    "```\n",
    "\n",
    "vertical distances:\n",
    "```\n",
    "    drC      (k_p1) float32 ...\n",
    "    drF      (k) float32 ...\n",
    "```        \n",
    "\n",
    "areas:\n",
    "```\n",
    "    rAs      (j_g, i) float32 ...\n",
    "    rAw      (j, i_g) float32 ...\n",
    "    rA       (j, i) float32 ...\n",
    "    rAz      (j_g, i_g) float32 ...\n",
    "```\n",
    "\n",
    "geometric factors\n",
    "```\n",
    "    hFacS    (k, j_g, i) float32 ...\n",
    "    hFacC    (k, j, i) float32 ...\n",
    "    hFacW    (k, j, i_g) float32 ...\n",
    "```    \n",
    "\n",
    "seafloor depth\n",
    "```\n",
    "Depth    (j, i) float32 ...\n",
    "```\n",
    "\n",
    "These ancilliary fields are classified as non-dimension coordinates because we wanted to reserve *data variables* for variables that are part of the model solution.\n",
    "\n",
    "In the end, it doesn't matter what you call these ancillary variables -- what matters is that you have them on hand for calculations.  If it makes you feel beter, use the function ``.reset_coords()`` to convert all non-dimension coordinates to data variables.  \n",
    "~~~\n",
    "grid_dataset.reset_coords()\n",
    "~~~\n",
    "\n",
    "### Non-Dimension Model Geometry Coordinates\n",
    "\n",
    "Non-Dimension Model Geometry \"Coordinates\" are not coordinates in the sense that they help you orient in space or time, but they provide measures of the model grid such as distances and areas.  Let's examine one of these grid geometric variables, *dxG*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'dxG' (tile: 13, j_g: 90, i: 90)>\n",
       "[105300 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g      (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * tile     (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    dxG      (tile, j_g, i) float32 ...\n",
       "    dyC      (tile, j_g, i) float32 ...\n",
       "    rAs      (tile, j_g, i) float32 ...\n",
       "Attributes:\n",
       "    standard_name:  cell_x_size_at_v_location\n",
       "    long_name:      cell x size\n",
       "    units:          m\n",
       "    coordinate:     YG XC"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dataset.dxG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``dxG`` has coordinates **tile**, **j_g** and **i** which means that it is a **v** location variable.  ``dxG`` is the horizontal distance between $g$ points (tracer cell corners) in the tile's $x$ direction.  \n",
    "\n",
    "For reference, see the chart below from the MITgcm documentation, [Figure 2.6](https://mitgcm.readthedocs.io/en/latest/algorithm/horiz-grid.html \"Fig 2.6\")\n",
    "\n",
    "dxG = $\\Delta x_g$ in subfigure (a) below:\n",
    "\n",
    "![hgrid](../figures/hgrid-abcd.svg)\n",
    "\n",
    "*Figure 2.6 Staggering of horizontal grid descriptors (lengths and areas). The grid lines indicate the tracer cell boundaries and are the reference grid for all panels. a) The area of a tracer cell, 𝐴𝑐, is bordered by the lengths Δ𝑥𝑔 and Δ𝑦𝑔. b) The area of a vorticity cell, 𝐴𝜁, is bordered by the lengths Δ𝑥𝑐 and Δ𝑦𝑐. c) The area of a u cell, 𝐴𝑤, is bordered by the lengths Δ𝑥𝑣 and Δ𝑦𝑓. d) The area of a v cell, 𝐴𝑠, is bordered by the lengths Δ𝑥𝑓 and Δ𝑦𝑢.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensions and Coordinates of *UVEL*\n",
    "\n",
    "So far we looked *THETA* which is a $c$ variable.  Let's examine *UVEL*, horizontal velocity in the tile's $x$ direction.  As you've probably guessed, *UVEL* is a $u$ variable: \n",
    "\n",
    "Load tile 2 of the 2010 March average horizonatal velocity in the $x$ direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Directory of the UVEL files\n",
    "data_dir= ECCO_dir + '/nctiles_monthly//UVEL/'\n",
    "\n",
    "fname = 'UVEL_2010.nc'\n",
    "uvel_dataset = xr.open_dataset(data_dir + fname).load()\n",
    "uvel_dataset.attrs = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``UVEL`` context\n",
    "\n",
    "Plot tile 1 time-mean horizontal velocity at the top-most model grid cell in the tile's $x$ direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f85133c1f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "ud_masked = uvel_dataset.UVEL.where(uvel_dataset.hFacW > 0, np.nan)\n",
    "ud_masked.isel(k=0,tile=1, time=0).plot(cmap='jet', vmin=-.2,vmax=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the dimensions and coordinates of *UVEL*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    UVEL     (time, tile, k, j, i_g) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvel_dataset.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * i_g        (i_g) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    dxC        (tile, j, i_g) float32 15583.418 15588.104 ... 23406.256\n",
       "    rAw        (tile, j, i_g) float32 361699460.0 362790240.0 ... 364760350.0\n",
       "    dyG        (tile, j, i_g) float32 23210.262 23273.26 ... 15595.26 15583.685\n",
       "    hFacW      (tile, k, j, i_g) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvel_dataset.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimension coordinates\n",
    "\n",
    "As expected, ``UVEL`` has horizontal dimensions of **i_g, j**. Because ``UVEL`` is the velocity at the left and right faces of 3D tracer cells.\n",
    "\n",
    "~~~\n",
    "  * i_g        (i_g) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
    "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
    "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
    "~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensions and Coordinates of *VVEL*\n",
    "\n",
    "Finally, let's examine *VVEL*, horizontal velocity in the tile's $y$ direction.  As you've probably guessed, *VVEL* is a $v$ variable: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Directory of the VVEL files\n",
    "data_dir= ECCO_dir + '/nctiles_monthly//VVEL/'\n",
    "\n",
    "fname = 'VVEL_2010.nc'\n",
    "vvel_dataset = xr.open_dataset(data_dir + fname).load()\n",
    "vvel_dataset.attrs = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``VVEL`` context\n",
    "\n",
    "Plot the time-mean horizontal velocity in the tile's $y$ direction at model level 5 (55m) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f85133156a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "vd_masked = vvel_dataset.VVEL.where(vvel_dataset.hFacS > 0, np.nan)\n",
    "vd_masked.isel(k=0,tile=1, time=0).plot(cmap='jet', vmin=-.2,vmax=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    VVEL     (time, tile, k, j_g, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vvel_dataset.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * j_g        (j_g) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    rAs        (tile, j_g, i) float32 179944260.0 180486990.0 ... 364150620.0\n",
       "    dxG        (tile, j_g, i) float32 15584.907 15589.316 ... 23142.107\n",
       "    dyC        (tile, j_g, i) float32 11563.718 11593.785 ... 15578.138\n",
       "    hFacS      (tile, k, j_g, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vvel_dataset.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimension coordinates\n",
    "\n",
    "As expected, *VVEL* has horizontal dimensions of **i, j_g**. Because *VVEL* is the velocity at the front and rear faces of 3D tracer cells we use **k** for its vertical coordinate.\n",
    "\n",
    "~~~\n",
    "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * j_g        (j_g) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
    "~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "ECCOv4 variables are on the staggered Arakawa-C grid.  Different dimension labels and coordinates are applied to state estimate variables so that one can easily identify where on the c-grid any particular variable is situated.\n"
   ]
  }
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